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A Bayesian attractor network with incremental learning
KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.
KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.ORCID-id: 0000-0002-2358-7815
KTH, Tidigare Institutioner, Numerisk analys och datalogi, NADA.ORCID-id: 0000-0002-2792-1622
2002 (engelsk)Inngår i: Network, ISSN 0954-898X, E-ISSN 1361-6536, Vol. 13, nr 2, s. 179-194Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

A realtime online learning system with capacity limits needs to gradually forget old information in order to avoid catastrophic forgetting. This can be achieved by allowing new information to overwrite old, as in a so-called palimpsest memory. This paper describes an incremental learning rule based on the Bayesian confidence propagation neural network that has palimpsest properties when employed in an attractor neural network. The network does not suffer from catastrophic forgetting, has a capacity dependent on the learning time constant and exhibits faster convergence for newer patterns.

sted, utgiver, år, opplag, sider
2002. Vol. 13, nr 2, s. 179-194
Emneord [en]
associative memory, neural networks, visual-cortex, hippocampal slices, potentiation, modulation, neurons, storage, specificity, palimpsests
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Identifikatorer
URN: urn:nbn:se:kth:diva-21564DOI: 10.1088/0954-898X/13/2/302ISI: 000175773700002OAI: oai:DiVA.org:kth-21564DiVA, id: diva2:340262
Merknad
QC 20100525Tilgjengelig fra: 2010-08-10 Laget: 2010-08-10 Sist oppdatert: 2018-01-12bibliografisk kontrollert

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